100 volumes of SIMULATION—20 years of DEVS research
Bibliographic record
Abstract
The growth of real-time embedded applications has surged in recent years, marked by both an increase in the number and complexity of tasks performed across various industries. Modeling and simulation (M&S) has been used for enhancing product quality while reducing lifecycle costs, primarily through improved testability and maintainability of real-time embedded systems applications, as M&S-based design approach allows for early hardware functionality testing, fosters collaboration between hardware and software teams, and shortens the product development cycle. The discrete-event system specification (DEVS) framework has been used for developing such discrete-event M&S systems. This article starts by highlighting the various DEVS publications in our journal over the past 20 years, reflecting the evolving landscape of simulation methodologies, which we organized into four categories: theory, methodology, tools, and applications. This curated selection reflects the diversity of topics and the evolution of scholarship within the field, encouraging further exploration and innovation. We conclude with recent research in the field, including our own research in real-time embedded systems development using DEVS software for modeling, simulation, and real-time execution of models. This paves the way for future discussions in this important field of research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".